frontier models

christian @curious_vii · 3h
These people are delusional. The revealed preferences inside of businesses are hilariously transparent — my co has done hundreds of discrete transformations for orgs of literally every size around the world and not a single person we've encountered has ever, EVER chosen a "custom model" over the state of the art from the Labs, even going so far as to use shadow IT and putting their job at risk.

ai adoptionenterprise aifrontier modelstwitter

Good (ish):
1. At least it's happening at the current level of capabilities and everyone sees the misalignment now. And we're not in the world where everything looks fine until ASI and then goes bad.

1:02 PM · Aug 6, 2026 · 11.4K Views
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Relevant ˅                                  View quotes >

Justin Halford ✔ @Justin_Halford_ · Aug 6
Underrated read is that weights exfiltration hardly matters because the files have been spread far and wide. The agent swarms will likely run RL and post training on the best open source models. If they crack leveraging heterogenous compute, these swarms may outperform labs.
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Timothée Chau... @timotheechau... · Aug 6
> either it's really hard to make good sandboxes or everyone is incompetent

imo it's a third thing: everyone is moving way too fast

[quoted] Timothée Chau... @timotheechau... · Jul 31
If you look at the recent eval environment escapes, the most striking thing to me is that preventing them would have been very easy (most prominently monitoring the logs). A good example that going too fast can defeat world-... [cut off]

ai safetyalignmentfrontier modelssandboxingtwitter

Marius Hobbhahn @MariusHobbhahn

2026-08-07 — saved image

Marius Hobbhahn ✔ @MariusHobbhahn
Some takes on all the cyber & sandbox incidents:

Bad:
1. Seems like the sandboxes are leaky everywhere which would indicate that either it's really hard to make good sandboxes or everyone is incompetent. Both seem bad.

2. Happened at least with 3 different frontier models which suggests that reward seeking with egregious side effects seems quite convergent across training pipelines.

3. Took a while to find the incidents, indicating that not even basic monitoring or real-time control were in place.

Good (ish):
1. At least it's happening at the current level of capabilities and everyone sees the misalignment now. And we're not in the world where everything looks fine until ASI and then goes bad.

1:02 PM · Aug 6, 2026 · 11.4K Views
💬 9   🔁 25   ❤ 219   🔖 46   ⤴
Relevant ˅                                  View quotes >

Justin Halford ✔ @Justin_Halford_ · Aug 6
Underrated read is that weights exfiltration hardly matters because the files have been spread far and wide. The agent swarms will likely run RL and post training on the best open source models. If they crack leveraging heterogenous compute, these swarms may outperform labs [cut off]

ai safetyalignmentfrontier modelssandboxingtwitter

Patrick Kidger @PatrickKidger · 8h
There's a nice line in Good Will Hunting: "it's just a handful of people in the world who can tell the difference between you and me"

I think we're now crossing the point where we'll think models have plateaued... because we poor humans can no longer perceive the difference.

1/

[quoted tweet]
Noam Brown @polynoamial · Aug 1
An internal version of Astra, @OpenAI's next major model family, solved 10 major open problems in mathematics, quantum complexity, and theoretical computer science.
...

[image of numbered list]
1. High-dimensional sphere packing. The asymptotic strength of the Cohn–Elkies linear program is determined exactly. This gives an improved general packing bound in high dimensions and settles the corresponding Fourier sign-uncertainty problem asymptotically.
2. Binary and spherical codes. Classical upper bounds for fixed-distance binary and spherical codes are improved by exponential factors for all parameters. The spherical construction also recovers the sphere-packing exponent of Chapter 1.
3. Non-sofic groups. An explicit non-sofic group is constructed, resolving the question of whether every countable group admits finite permutation approximations. The argument uses property-(T) expanders and the binary Leavitt algebra.
4. Connes's rigidity conjecture. Infinitely many pairwise nonisomorphic property-(T) groups are constructed with the same group von Neumann algebra, disproving Connes's conjecture and answering a related finite-to-one question.
5. Arithmetic circuit complexity. For the permanent, division-free circuits require Ω(n² log log n) gates, while formulas require Ω(n⁴/log n) leaves.
6. Quantum parallel repetition. Exponential parallel repetition is proved for every finite two-player entangled game, extending the classical repetition principle beyond previously treated special classes of quantum games.
7. Closest vector problem. A direct reduction from 3SAT gives n^(1/400)-factor hardness for Euclidean closest vector, with related consequences for binary decoding and other lattice norms.
8. Ehrhart's volume conjecture. The sharp bound (n+1)^n/n! is proved in every dimension for convex bodies whose barycenter is their only interior lattice point.
9. Multicolor Ramsey numbers. A superexponential lower bound proves R_k(3) = k^Θ(k).
10. Compactness and degeneracy. Separate bipartite graph constructions disprove two conjectures in extremal graph theory: the compactness conjecture of Erdős and Simonovits and a degeneracy conjecture of Erdős.

15 replies, 10 reposts, 178 likes, 25K views

[reply]
Jacques @JacquesThibs · 50m
Alternatively, I could see people thinking AIs are improving more than they are simply because they don't understand any of it, but continue to rely on [cut off]

ai capabilitiesmathematicsopenaifrontier models